Column

About

This dashboard shows results from Wave 0 of the Berkeley Interpersonal Contact Study (BICS) in Spring 2020.

Caveats / underway:

  • except where noted, these results show the national and city samples pooled together

  • the pooled estimates have been weighted to improve sample representativeness. Weights are based on age, sex, race/ethnicity, household size, and urbanicity

  • we are currently collecting data and will post more recent estimates as soon as we can

If you have questions or are interested in funding this study, please contact us at .

Initial support provided by a Berkeley Population Center pilot grant (NICHD P2CHD073964). This project has been approved by the UC Berkeley IRB (Protocol 2020-03-13128).

Updates

Wave 0 results updated 2020-04-22

2020-04-22:

  • Added an estimated mixing matrix that has symmetrization enforced

2020-04-20:

  • Most plots are now weighted to improve sample representativeness using raking. (These weights have made little difference to the previous estimates)

2020-04-17:

Column

Number of conversational contacts

Number of conversational contacts

Number of conversational contacts outside the household

Conversational contacts by age

Conversational contacts outside household by age

Mixing

NB: please see the ‘data’ tab if you want the numbers behind these mixing estimates

By age/sex

By age/sex - non-hh contacts

By age/sex - non-hh contacts, symmetric

The matrix calculated below uses the symmetrization formula found in the vignette for the socialmixr package.
(We calculate it by hand, since the package was not designed for data collected using our instrument.) We used the 2018 American Community Survey to obtain the national age-distribution.

Comparison - all conversational contacts

# A tibble: 16 x 6
# Groups:   ego_age [4]
   ego_age  alter_age  bics    fb ratio frac_decrease
   <chr>    <chr>     <dbl> <dbl> <dbl>         <dbl>
 1 [25,35)  [25,35)   0.887 7.16  0.124         0.876
 2 [25,35)  [35,45)   0.578 2.36  0.245         0.755
 3 [25,35)  [45,65)   0.397 1.25  0.319         0.681
 4 [25,35)  [65,100]  0.105 0.173 0.604         0.396
 5 [35,45)  [25,35)   0.522 3.29  0.159         0.841
 6 [35,45)  [35,45)   1.11  5.87  0.189         0.811
 7 [35,45)  [45,65)   0.381 1.70  0.224         0.776
 8 [35,45)  [65,100]  0.234 0.528 0.443         0.557
 9 [45,65)  [25,35)   0.315 2.23  0.141         0.859
10 [45,65)  [35,45)   0.400 3.10  0.129         0.871
11 [45,65)  [45,65)   0.907 3.77  0.241         0.759
12 [45,65)  [65,100]  0.280 0.755 0.371         0.629
13 [65,100] [25,35)   0.186 0.737 0.252         0.748
14 [65,100] [35,45)   0.247 2.14  0.115         0.885
15 [65,100] [45,65)   0.527 2.00  0.263         0.737
16 [65,100] [65,100]  0.837 2.17  0.385         0.615

Relationships

Relationships, non-household contacts

Locations

Locations - non-household contacts

Contact durations - by relationship

Contact durations - by respondent age

COVID-19

Awareness

Concern

Behavior change

Cities

Number of interviews

Conversational contacts

Conversational contacts outside of household

Model

Overview

To help summarize patterns in the contact survey data, we fit a negative binomial model, accounting for the right-censoring of reported contacts at 10. These models show relationships among people who have completed the survey; have not been adjusted in any way for sampling. We fit these models using the brms package in R.

We modeled the expected log number of reported contacts as a function of age group, city, gender, and household size. The plots below show posterior means and 95% credible intervals for the estimated coefficients. Estimated coefficients greater than 0 imply that the predictor is associated with higher reported numbers of contacts, while estimated coefficients less than 0 imply that the predictor is associated with lower reported numbers of contacts.

There are two models: one for total number of contacts, and one for the number of non-household contacts.

Negative-binomial model for household contacts

Negative-binomial model for non-household contacts

Technical details

Coming soon.

Respondent characteristics

Weights

The pooled estimates have been weighted using raking to improve sample representativeness. Weights are based on

  • Age
  • Sex
  • Age/sex interaction
  • Race (Black, White, Other)
  • Hispanic status
  • Household size (1/2/3/4/5+)
  • Urbanicity

Population values are taken from the 2018 ACS, estimated from an IPUMS USA extract. We used the R packages ipumsr, leafpeepr and autumn to help perform the raking.

Number of interviews by date

Age/sex

Race/ethnicity

Household size

Contact definition

Respondents to the survey were told to consider someone a contact using this text:

We would like to ask you some questions about people you had in-person conversational contact with yesterday.

By in-person conversational contact, we mean a two-way conversation with three or more words in the physical presence of another person.

You might have conversational contact with family members, friends, co-workers, store clerks, bus drivers, and so forth.

(Please do not count people you contacted exclusively by telephone, text, or online. Only consider people you interacted with face-to-face.)

 

Data

Microdata

We plan to make a version of the data with no identifying information publicly available as soon as we can. If you are a disease modeler who urgently needs to see the microdata, please reach out to us by email.

The estimated mixing matrices are reproduced as tables below. Note that these are the crude estimates, and have not had a symmetry constraint enforced.

All contact mixing estimates

ego_age alter_age weighted_n raw_n num_interviews weighted_num_interviews avg_per_ego
[18,25) [0,10) 37.716470 25 185 168.6894 0.2235853
[18,25) [10,18) 74.773497 51 185 168.6894 0.4432615
[18,25) [18,25) 178.608365 154 185 168.6894 1.0588004
[18,25) [25,35) 71.192006 65 185 168.6894 0.4220302
[18,25) [35,45) 81.291936 49 185 168.6894 0.4819031
[18,25) [45,65) 120.444585 93 185 168.6894 0.7140022
[18,25) [65,100] 7.896276 9 185 168.6894 0.0468096
[25,35) [0,10) 117.912685 60 280 284.8089 0.4140063
[25,35) [10,18) 39.070362 29 280 284.8089 0.1371810
[25,35) [18,25) 111.530325 59 280 284.8089 0.3915970
[25,35) [25,35) 252.513878 243 280 284.8089 0.8866080
[25,35) [35,45) 164.604164 105 280 284.8089 0.5779459
[25,35) [45,65) 113.138421 106 280 284.8089 0.3972432
[25,35) [65,100] 29.768574 27 280 284.8089 0.1045212
[35,45) [0,10) 47.696865 59 300 238.7979 0.1997374
[35,45) [10,18) 93.895477 75 300 238.7979 0.3932007
[35,45) [18,25) 47.982310 37 300 238.7979 0.2009328
[35,45) [25,35) 124.600333 117 300 238.7979 0.5217816
[35,45) [35,45) 264.969790 303 300 238.7979 1.1095987
[35,45) [45,65) 91.026618 92 300 238.7979 0.3811869
[35,45) [65,100] 55.883816 54 300 238.7979 0.2340214
[45,65) [0,10) 54.572730 32 456 452.7175 0.1205448
[45,65) [10,18) 117.993955 91 456 452.7175 0.2606348
[45,65) [18,25) 104.273891 81 456 452.7175 0.2303288
[45,65) [25,35) 142.747106 122 456 452.7175 0.3153116
[45,65) [35,45) 181.174330 156 456 452.7175 0.4001929
[45,65) [45,65) 410.472184 359 456 452.7175 0.9066850
[45,65) [65,100] 126.710894 130 456 452.7175 0.2798895
[65,100] [0,10) 25.494972 12 214 289.9863 0.0879178
[65,100] [10,18) 25.919224 10 214 289.9863 0.0893808
[65,100] [18,25) 28.853646 21 214 289.9863 0.0995000
[65,100] [25,35) 53.940909 44 214 289.9863 0.1860119
[65,100] [35,45) 71.527564 59 214 289.9863 0.2466584
[65,100] [45,65) 152.828959 83 214 289.9863 0.5270213
[65,100] [65,100] 242.793731 148 214 289.9863 0.8372592

Non-household mixing estimates

ego_age alter_age weighted_n raw_n num_interviews weighted_num_interviews ego_acs_N alter_acs_N unadj_avg_per_ego other_unadj_avg_per_ego sym_avg_per_ego
[18,25) [0,10) 4.017945 8 185 168.6894 30.64812 NA 0.0238186 NA 0.0238186
[18,25) [10,18) 8.323148 11 185 168.6894 30.64812 NA 0.0493401 NA 0.0493401
[18,25) [18,25) 78.002468 61 185 168.6894 30.64812 30.64812 0.4624030 0.4624030 0.4624030
[18,25) [25,35) 38.349818 29 185 168.6894 30.64812 45.27702 0.2273399 0.2031270 0.2637115
[18,25) [35,45) 13.343623 13 185 168.6894 30.64812 41.68729 0.0791017 0.0987162 0.1066873
[18,25) [45,65) 1.948754 9 185 168.6894 30.64812 83.87452 0.0115523 0.0879824 0.1261666
[18,25) [65,100] 5.412259 4 185 168.6894 30.64812 52.40755 0.0320842 0.0219125 0.0347770
[25,35) [0,10) 15.248611 4 280 284.8089 45.27702 NA 0.0535398 NA 0.0535398
[25,35) [10,18) 4.522862 4 280 284.8089 45.27702 NA 0.0158803 NA 0.0158803
[25,35) [18,25) 57.852379 25 280 284.8089 45.27702 30.64812 0.2031270 0.2273399 0.1785069
[25,35) [25,35) 89.912526 96 280 284.8089 45.27702 45.27702 0.3156942 0.3156942 0.3156942
[25,35) [35,45) 49.347352 43 280 284.8089 45.27702 41.68729 0.1732648 0.2636725 0.2080162
[25,35) [45,65) 37.579099 41 280 284.8089 45.27702 83.87452 0.1319449 0.1943114 0.2459510
[25,35) [65,100] 12.034939 13 280 284.8089 45.27702 52.40755 0.0422562 0.1403902 0.1023780
[35,45) [0,10) 1.569671 3 300 238.7979 41.68729 NA 0.0065732 NA 0.0065732
[35,45) [10,18) 3.795683 4 300 238.7979 41.68729 NA 0.0158950 NA 0.0158950
[35,45) [18,25) 23.573205 21 300 238.7979 41.68729 30.64812 0.0987162 0.0791017 0.0784355
[35,45) [25,35) 62.964420 55 300 238.7979 41.68729 45.27702 0.2636725 0.1732648 0.2259286
[35,45) [35,45) 81.135082 83 300 238.7979 41.68729 41.68729 0.3397647 0.3397647 0.3397647
[35,45) [45,65) 50.656130 50 300 238.7979 41.68729 83.87452 0.2121298 0.2223428 0.3297409
[35,45) [65,100] 18.537085 20 300 238.7979 41.68729 52.40755 0.0776267 0.1250806 0.1174364
[45,65) [0,10) 4.745827 5 456 452.7175 83.87452 NA 0.0104830 NA 0.0104830
[45,65) [10,18) 9.348913 5 456 452.7175 83.87452 NA 0.0206507 NA 0.0206507
[45,65) [18,25) 39.831176 22 456 452.7175 83.87452 30.64812 0.0879824 0.0115523 0.0461018
[45,65) [25,35) 87.968182 73 456 452.7175 83.87452 45.27702 0.1943114 0.1319449 0.1327689
[45,65) [35,45) 100.658469 91 456 452.7175 83.87452 41.68729 0.2223428 0.2121298 0.1638877
[45,65) [45,65) 142.601754 128 456 452.7175 83.87452 83.87452 0.3149906 0.3149906 0.3149906
[45,65) [65,100] 54.693567 59 456 452.7175 83.87452 52.40755 0.1208117 0.2976326 0.1533911
[65,100] [0,10) 20.586516 8 214 289.9863 52.40755 NA 0.0709913 NA 0.0709913
[65,100] [10,18) 17.498335 7 214 289.9863 52.40755 NA 0.0603419 NA 0.0603419
[65,100] [18,25) 6.354339 9 214 289.9863 52.40755 30.64812 0.0219125 0.0320842 0.0203377
[65,100] [25,35) 40.711233 32 214 289.9863 52.40755 45.27702 0.1403902 0.0422562 0.0884485
[65,100] [35,45) 36.271668 36 214 289.9863 52.40755 41.68729 0.1250806 0.0776267 0.0934142
[65,100] [45,65) 86.309371 46 214 289.9863 52.40755 83.87452 0.2976326 0.1208117 0.2454915
[65,100] [65,100] 75.041648 49 214 289.9863 52.40755 52.40755 0.2587765 0.2587765 0.2587765